As expected, the performance error between the optimal policy and the MPC policy is affected by the model approximation error \(\epsilon _f\) , given a prediction horizon \(H\) . As we will show in the following, the maximum entropy exploration of SAC [1]} can acquire more expressive dynamics models by visiting unmodeled transitions in the environment; together with the approximation of the VF, we can leverage the bound of (REF ) to acquire near-optimal trajectories for policy learning.
